Enhancing Students' Retention Through Automated Mobile-Based Micro-Learning
This research project aims to address the issue of students' retention of concepts and skills by leveraging mobile devices for micro-learning. By breaking down lecture content into bite-sized messages and scheduling their delivery throughout the day, the study seeks to improve students' ability to retain information in various subjects. The methodology includes E-tests, text messages, images, and short videos to reinforce learning. The related work explores different techniques and technologies used to enhance students' retention across subjects like math, statistics, languages, and IT.
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An Automated Mobile-Based Micro- Learning to Enhance Students Retention of Concepts and Skills Hasan Kadhem IT Department College of IT hkadhem@ahlia.edu.bh Ahlia Research Forum 22nd April 2018
Outline Introduction The Problem Solution Overview Related Work Methodology Results Challenges Conclusion
Introduction Introduction Problem Solution Related Work Methodology Results Challenges Conclusion The use of mobile devices has become a part of our daily routine. Researchers and teachers have been exploring different ways to use mobile devices in education to support learning and teaching.
The Problem Introduction Problem Solution Related Work Methodology Results Challenges Conclusion The ability of students to retain information for future use is worthwhile in any subject. Students retention of concepts and skills is a noticeable problem in different subjects Concepts and theories are built upon each other as students progress through a curriculum and courses.
Solution Overview Introduction Problem Solution Related Work Methodology Results Challenges Conclusion Our approach to improve students retention: Delivering micro-learning through mobile devices. Convert the lecture content into multiple bite-sized chunks, then send them to students mobile devices at different times during a day.
Related Work Introduction Problem Solution Related Work Methodology Results Challenges Conclusion There are many related literatures to improve students retention of concept and skills for different subjects such as math, statistics, languages and IT: Different assessment techniques, one-minute paper, web-based micro-questions, and group discussion. Many researchers studied the use of mobile apps to support IT education: Most of these studied and apps are designed just to teach programming courses. The effect of using mobile based assessments and evaluation. The effect of using mobile-based games on student s performance.
Methodology Introduction Problem Solution Related Work Methodology Results Challenges Conclusion Two set of E-test on lecture content is summarized and chunked into 24 messages (Text: 140 character, Images, and short video <3 minutes) Schedule micro-content from 8:00 AM to 8:00 PM First Cycle: Lecture (CPU Scheduling) Followed by 10 minutes test Micro-content messages No micro- content messages Second Cycle: Lecture (Storage Management) Followed by 10 minutes test Two topics: (a) CPU Scheduling Moodle Different questions, but evaluate same ILOs 6 questions: 4 multiple-choice questions to evaluate A1 messages to be sent to all students from 8:00 4th level course operating system 22 Students (b) Storage Management Two ILOs: (A1) Concepts and Theories (B1) Problem Solving 2 fill in the blank questions to evaluate B1 AM to 8:00 PM
Results Introduction Problem Solution Related Work Methodology Results Challenges Conclusion Average retention rate: 105.53 110 110 100.95 100 100 90 90 80 72.37 80 Average Retention % Average Retention % 68.63 70 70 62.50 60 60 45.21 50 50 40 40 30 30 20 20 10 10 0 0 A1 B1 Overall A1 B1 Overall ILOs ILOs (b) Second cycle (No Micro-learning) (a) First cycle (Mobile Micro-learning)
Results Introduction Problem Solution Related Work Methodology Results Challenges Conclusion Messages read rate: 20 18 16 14 No. of reads 12 10 8 6 4 2 0 Message Time
Challenges Introduction Problem Solution Related Work Methodology Results Challenges Conclusion Time: chunking lecture notes into small parts, and scheduling messages. Students have different time table and various activities, so sending messages for all students at the same time is not effective. Requirements for an automated solution are: Ability to summarize and chunk lecture notes into micro- content messages, considering different messages types (text, images, and video). Ability to learn from student s read history records to schedule future messages.
Challenges Introduction Problem Solution Related Work Methodology Results Challenges Conclusion Small Messages Summary, Related Multimedia Text Lecture Notes Text Images Videos Image Video Automatically Scheduling
Challenges Introduction Problem Solution Related Work Methodology Results Challenges Conclusion From an IT perspective, these challenges and requirements involves researches in different areas: Summarization and text mining techniques for automatic chunking. Big data research to identify the most related materials (multimedia content such as videos, and images) to the lecture content. Machine learning algorithm to automatically scheduling messages for different students based on the best readable time.
Conclusion Introduction Problem Solution Related Work Methodology Results Challenges Conclusion We conduct a well-defined case study to measure the effectiveness of using mobile-based micro- content messages on students retention. The results show a noticeable improvement on student retention using mobile micro-learning compared with the normal lecture cycle. We propose an important IT challenges and research directions for developing an automated solution for mobile micro-learning that supports both student and instructor.
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